# load libraries

	library(stargazer)

# load data

	setwd("Z:\\projects\\yellow_pages")
	cc <- read.table("chatChip.txt", sep="\t", header=TRUE)

# simplify names

	cc$fcq <- cc$FishandChipShopsandRestaurantsQuotient

	cc$jrq <- cc$JapaneseRestaurantsQuotient
	
# models for Table 2
# these are based on the things with the 
# strongest brexit correlations

	m1a <- lm(brexitShare ~ jrq + c11QualLevel4 + c11NSSECSemiRoutine + c11IndustryWholesale + c11HealthVeryGood + c11BornOtherPre2004EU, data=cc)

	m1b <- lm(brexitShare ~ fcq + c11QualLevel4 + c11NSSECSemiRoutine + c11IndustryWholesale + c11HealthVeryGood + c11BornOtherPre2004EU, data=cc)

	m1c1 <- lm(brexitShare ~ restaurantDiversity + c11QualLevel4 + c11NSSECSemiRoutine + c11IndustryWholesale + c11HealthVeryGood + c11BornOtherPre2004EU, data=cc)
	
	m1c2 <- lm(brexitShare ~ jrq + restaurantDiversity + c11QualLevel4 + c11NSSECSemiRoutine + c11IndustryWholesale + c11HealthVeryGood + c11BornOtherPre2004EU, data=cc)

	m1d1 <- lm(brexitShare ~ FastFoodRestaurantsQuotient + c11QualLevel4 + c11NSSECSemiRoutine + c11IndustryWholesale + c11HealthVeryGood + c11BornOtherPre2004EU, data=cc)
	
	m1d2 <- lm(brexitShare ~ FastFoodRestaurantsQuotient + fcq + c11QualLevel4 + c11NSSECSemiRoutine + c11IndustryWholesale + c11HealthVeryGood + c11BornOtherPre2004EU, data=cc)


stargazer(m1a, m1c1, m1c2, m1b, m1d1, m1d2, omit.stat=c("f", "ser"), covariate.labels = c(
	"Japanese restaurants (prop.)", 
	"Fish and chips (prop.)", 
	"Restaurant diversity", 
	"Fast food (prop.)", 
	"Highest edu: L4", 
	"Work: semi-routine", 
	"Industry: wholesale", 
	"Health: very good", 
	"Born: other pre-2004 EU", 
	"(Intercept)"
	), dep.var.labels = "Brexit share", column.labels=c("Model 1a", "Model 1c.1", "Model 1c.2", "Model 1b", "Model 1d.1", "Model 1d.2"), type="text")


